'''simple docstring'''




__UpperCAmelCase					=frozenset(
    [
        "prompt",
        "height",
        "width",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
        "cross_attention_kwargs",
    ]
)

__UpperCAmelCase					=frozenset(["prompt", "negative_prompt"])

__UpperCAmelCase					=frozenset([])

__UpperCAmelCase					=frozenset(["image"])

__UpperCAmelCase					=frozenset(
    [
        "image",
        "height",
        "width",
        "guidance_scale",
    ]
)

__UpperCAmelCase					=frozenset(["image"])

__UpperCAmelCase					=frozenset(
    [
        "prompt",
        "image",
        "height",
        "width",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
    ]
)

__UpperCAmelCase					=frozenset(["prompt", "image", "negative_prompt"])

__UpperCAmelCase					=frozenset(
    [
        # Text guided image variation with an image mask
        "prompt",
        "image",
        "mask_image",
        "height",
        "width",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
    ]
)

__UpperCAmelCase					=frozenset(["prompt", "image", "mask_image", "negative_prompt"])

__UpperCAmelCase					=frozenset(
    [
        # image variation with an image mask
        "image",
        "mask_image",
        "height",
        "width",
        "guidance_scale",
    ]
)

__UpperCAmelCase					=frozenset(["image", "mask_image"])

__UpperCAmelCase					=frozenset(
    [
        "example_image",
        "image",
        "mask_image",
        "height",
        "width",
        "guidance_scale",
    ]
)

__UpperCAmelCase					=frozenset(["example_image", "image", "mask_image"])

__UpperCAmelCase					=frozenset(["class_labels"])

__UpperCAmelCase					=frozenset(["class_labels"])

__UpperCAmelCase					=frozenset(["batch_size"])

__UpperCAmelCase					=frozenset([])

__UpperCAmelCase					=frozenset(["batch_size"])

__UpperCAmelCase					=frozenset([])

__UpperCAmelCase					=frozenset(
    [
        "prompt",
        "audio_length_in_s",
        "guidance_scale",
        "negative_prompt",
        "prompt_embeds",
        "negative_prompt_embeds",
        "cross_attention_kwargs",
    ]
)

__UpperCAmelCase					=frozenset(["prompt", "negative_prompt"])
__UpperCAmelCase					=frozenset(["input_tokens"])

__UpperCAmelCase					=frozenset(["input_tokens"])

'''simple docstring'''




import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


__UpperCAmelCase					=logging.getLogger(__name__)
__UpperCAmelCase					=list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__UpperCAmelCase					=tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class        a__ :
     lowerCamelCase    :	Optional[str]				=field(
         default=UpperCAmelCase__				,  metadata={
             "help": (
                 "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
             )
         }				,  )
     lowerCamelCase    :	Optional[str]				=field(
         default=UpperCAmelCase__				,  metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(UpperCAmelCase__					)}				,  )
     lowerCamelCase    :	Optional[str]				=field(
         default=UpperCAmelCase__				,  metadata={
             "help": (
                 "Override some existing default config settings when a model is trained from scratch. Example: "
                 "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
             )
         }				,  )
     lowerCamelCase    :	Optional[str]				=field(
         default=UpperCAmelCase__				,  metadata={"help": "Pretrained config name or path if not the same as model_name"}					)
     lowerCamelCase    :	Optional[str]				=field(
         default=UpperCAmelCase__				,  metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}					)
     lowerCamelCase    :	Optional[str]				=field(
         default=UpperCAmelCase__				,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}				,  )
     lowerCamelCase    :	bool				=field(
         default=UpperCAmelCase__				,  metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}				,  )
     lowerCamelCase    :	str				=field(
         default="main"				,  metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}				,  )
     lowerCamelCase    :	bool				=field(
         default=UpperCAmelCase__				,  metadata={
             "help": (
                 "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                 "with private models)."
             )
         }				,  )






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[str]       ):





         """simple docstring"""



         if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
             raise ValueError(
                 '''--config_overrides can\'t be used in combination with --config_name or --model_name_or_path'''       )




@dataclass
class        a__ :
     lowerCamelCase    :	Optional[str]				=field(
         default=UpperCAmelCase__				,  metadata={"help": "The name of the dataset to use (via the datasets library)."}					)
     lowerCamelCase    :	Optional[str]				=field(
         default=UpperCAmelCase__				,  metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}					)
     lowerCamelCase    :	Optional[str]				=field(default=UpperCAmelCase__				,  metadata={"help": "The input training data file (a text file)."}					)
     lowerCamelCase    :	Optional[str]				=field(
         default=UpperCAmelCase__				,  metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}				,  )
     lowerCamelCase    :	Optional[str]				=field(
         default=UpperCAmelCase__				,  metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}				,  )
     lowerCamelCase    :	Optional[str]				=field(
         default=UpperCAmelCase__				,  metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}				,  )
     lowerCamelCase    :	bool				=field(
         default=UpperCAmelCase__				,  metadata={"help": "Overwrite the cached training and evaluation sets"}					)
     lowerCamelCase    :	Optional[int]				=field(
         default=5				,  metadata={
             "help": "The percentage of the train set used as validation set in case there's no validation split"
         }				,  )
     lowerCamelCase    :	Optional[int]				=field(
         default=UpperCAmelCase__				,  metadata={
             "help": (
                 "The maximum total input sequence length after tokenization. Sequences longer "
                 "than this will be truncated. Default to the max input length of the model."
             )
         }				,  )
     lowerCamelCase    :	Optional[int]				=field(
         default=UpperCAmelCase__				,  metadata={"help": "The number of processes to use for the preprocessing."}				,  )
     lowerCamelCase    :	float				=field(
         default=0.15				,  metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}					)
     lowerCamelCase    :	bool				=field(
         default=UpperCAmelCase__				,  metadata={
             "help": (
                 "Whether to pad all samples to `max_seq_length`. "
                 "If False, will pad the samples dynamically when batching to the maximum length in the batch."
             )
         }				,  )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         if self.train_file is not None:
             __lowerCamelCase            =    self.train_file.split('''.'''       )[-1]
             assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
         if self.validation_file is not None:
             __lowerCamelCase            =    self.validation_file.split('''.'''       )[-1]
             assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[Any]:
    with open(UpperCamelCase__							,    '''r'''							,    encoding='''utf-8'''							) as f:
        __lowerCamelCase            =    [json.loads(UpperCamelCase__							) for line in f.read().splitlines() if (len(UpperCamelCase__							) > 0 and not line.isspace())]
    assert len(UpperCamelCase__							) == len(UpperCamelCase__							)

    __lowerCamelCase            =    {c: dataset[c] for c in dataset.column_names}
    __lowerCamelCase            =    refs
    return Dataset.from_dict(UpperCamelCase__							)






def __lowerCAmelCase					(				)      ->       List[str]:
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    __lowerCamelCase            =    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
    if len(sys.argv							) == 2 and sys.argv[1].endswith('''.json'''							):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
    else:
        __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    parser.parse_args_into_dataclasses()

    # Detecting last checkpoint.
    __lowerCamelCase            =    None
    if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
        __lowerCamelCase            =    get_last_checkpoint(training_args.output_dir							)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir							)							) > 0:
            raise ValueError(
                f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
                '''Use --overwrite_output_dir to overcome.'''							)
        elif last_checkpoint is not None:
            logger.info(
                f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
                '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''							)

    # Setup logging
    logging.basicConfig(
        format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''							,    datefmt='''%m/%d/%Y %H:%M:%S'''							,    handlers=[logging.StreamHandler(sys.stdout							)]							,    )
    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank							) else logging.WARN							)

    # Log on each process the small summary:
    logger.warning(
        f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
        + f"""distributed training: {bool(training_args.local_rank != -1							)}, 16-bits training: {training_args.fpaa}"""							)
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank							):
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info('''Training/evaluation parameters %s'''							,    UpperCamelCase__							)

    # Set seed before initializing model.
    set_seed(training_args.seed							)

    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
    # 'text' is found. You can easily tweak this behavior (see below).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        __lowerCamelCase            =    load_dataset(data_args.dataset_name							,    data_args.dataset_config_name							)
        if "validation" not in datasets.keys():
            __lowerCamelCase            =    load_dataset(
                data_args.dataset_name							,    data_args.dataset_config_name							,    split=f"""train[:{data_args.validation_split_percentage}%]"""							,    )
            __lowerCamelCase            =    load_dataset(
                data_args.dataset_name							,    data_args.dataset_config_name							,    split=f"""train[{data_args.validation_split_percentage}%:]"""							,    )
    else:
        __lowerCamelCase            =    {}
        if data_args.train_file is not None:
            __lowerCamelCase            =    data_args.train_file
        if data_args.validation_file is not None:
            __lowerCamelCase            =    data_args.validation_file
        __lowerCamelCase            =    data_args.train_file.split('''.'''							)[-1]
        if extension == "txt":
            __lowerCamelCase            =    '''text'''
        __lowerCamelCase            =    load_dataset(UpperCamelCase__							,    data_files=UpperCamelCase__							)
    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    __lowerCamelCase            =    {
        '''cache_dir''': model_args.cache_dir,
        '''revision''': model_args.model_revision,
        '''use_auth_token''': True if model_args.use_auth_token else None,
    }
    if model_args.config_name:
        __lowerCamelCase            =    AutoConfig.from_pretrained(model_args.config_name							,    **UpperCamelCase__							)
    elif model_args.model_name_or_path:
        __lowerCamelCase            =    AutoConfig.from_pretrained(model_args.model_name_or_path							,    **UpperCamelCase__							)
    else:
        __lowerCamelCase            =    CONFIG_MAPPING[model_args.model_type]()
        logger.warning('''You are instantiating a new config instance from scratch.'''							)
        if model_args.config_overrides is not None:
            logger.info(f"""Overriding config: {model_args.config_overrides}"""							)
            config.update_from_string(model_args.config_overrides							)
            logger.info(f"""New config: {config}"""							)

    __lowerCamelCase            =    {
        '''cache_dir''': model_args.cache_dir,
        '''use_fast''': model_args.use_fast_tokenizer,
        '''revision''': model_args.model_revision,
        '''use_auth_token''': True if model_args.use_auth_token else None,
    }
    if model_args.tokenizer_name:
        __lowerCamelCase            =    AutoTokenizer.from_pretrained(model_args.tokenizer_name							,    **UpperCamelCase__							)
    elif model_args.model_name_or_path:
        __lowerCamelCase            =    AutoTokenizer.from_pretrained(model_args.model_name_or_path							,    **UpperCamelCase__							)
    else:
        raise ValueError(
            '''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
            '''You can do it from another script, save it, and load it from here, using --tokenizer_name.'''							)

    if model_args.model_name_or_path:
        __lowerCamelCase            =    AutoModelForMaskedLM.from_pretrained(
            model_args.model_name_or_path							,    from_tf=bool('''.ckpt''' in model_args.model_name_or_path							)							,    config=UpperCamelCase__							,    cache_dir=model_args.cache_dir							,    revision=model_args.model_revision							,    use_auth_token=True if model_args.use_auth_token else None							,    )
    else:
        logger.info('''Training new model from scratch'''							)
        __lowerCamelCase            =    AutoModelForMaskedLM.from_config(UpperCamelCase__							)

    model.resize_token_embeddings(len(UpperCamelCase__							)							)

    # Preprocessing the datasets.
    # First we tokenize all the texts.
    if training_args.do_train:
        __lowerCamelCase            =    datasets['''train'''].column_names
    else:
        __lowerCamelCase            =    datasets['''validation'''].column_names
    __lowerCamelCase            =    '''text''' if '''text''' in column_names else column_names[0]

    __lowerCamelCase            =    '''max_length''' if data_args.pad_to_max_length else False

    def tokenize_function(UpperCamelCase__							):
        # Remove empty lines
        __lowerCamelCase            =    [line for line in examples['''text'''] if len(UpperCamelCase__							) > 0 and not line.isspace()]
        return tokenizer(examples['''text''']							,    padding=UpperCamelCase__							,    truncation=UpperCamelCase__							,    max_length=data_args.max_seq_length							)

    __lowerCamelCase            =    datasets.map(
        UpperCamelCase__							,    batched=UpperCamelCase__							,    num_proc=data_args.preprocessing_num_workers							,    remove_columns=[text_column_name]							,    load_from_cache_file=not data_args.overwrite_cache							,    )

    # Add the chinese references if provided
    if data_args.train_ref_file is not None:
        __lowerCamelCase            =    add_chinese_references(tokenized_datasets['''train''']							,    data_args.train_ref_file							)
    if data_args.validation_ref_file is not None:
        __lowerCamelCase            =    add_chinese_references(
            tokenized_datasets['''validation''']							,    data_args.validation_ref_file							)
    # If we have ref files, need to avoid it removed by trainer
    __lowerCamelCase            =    data_args.train_ref_file or data_args.validation_ref_file
    if has_ref:
        __lowerCamelCase            =    False

    # Data collator
    # This one will take care of randomly masking the tokens.
    __lowerCamelCase            =    DataCollatorForWholeWordMask(tokenizer=UpperCamelCase__							,    mlm_probability=data_args.mlm_probability							)

    # Initialize our Trainer
    __lowerCamelCase            =    Trainer(
        model=UpperCamelCase__							,    args=UpperCamelCase__							,    train_dataset=tokenized_datasets['''train'''] if training_args.do_train else None							,    eval_dataset=tokenized_datasets['''validation'''] if training_args.do_eval else None							,    tokenizer=UpperCamelCase__							,    data_collator=UpperCamelCase__							,    )

    # Training
    if training_args.do_train:
        if last_checkpoint is not None:
            __lowerCamelCase            =    last_checkpoint
        elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path							):
            __lowerCamelCase            =    model_args.model_name_or_path
        else:
            __lowerCamelCase            =    None
        __lowerCamelCase            =    trainer.train(resume_from_checkpoint=UpperCamelCase__							)
        trainer.save_model()  # Saves the tokenizer too for easy upload

        __lowerCamelCase            =    os.path.join(training_args.output_dir							,    '''train_results.txt'''							)
        if trainer.is_world_process_zero():
            with open(UpperCamelCase__							,    '''w'''							) as writer:
                logger.info('''***** Train results *****'''							)
                for key, value in sorted(train_result.metrics.items()							):
                    logger.info(f"""  {key} = {value}"""							)
                    writer.write(f"""{key} = {value}\n"""							)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
            trainer.state.save_to_json(os.path.join(training_args.output_dir							,    '''trainer_state.json'''							)							)

    # Evaluation
    __lowerCamelCase            =    {}
    if training_args.do_eval:
        logger.info('''*** Evaluate ***'''							)

        __lowerCamelCase            =    trainer.evaluate()

        __lowerCamelCase            =    math.exp(eval_output['''eval_loss''']							)
        __lowerCamelCase            =    perplexity

        __lowerCamelCase            =    os.path.join(training_args.output_dir							,    '''eval_results_mlm_wwm.txt'''							)
        if trainer.is_world_process_zero():
            with open(UpperCamelCase__							,    '''w'''							) as writer:
                logger.info('''***** Eval results *****'''							)
                for key, value in sorted(results.items()							):
                    logger.info(f"""  {key} = {value}"""							)
                    writer.write(f"""{key} = {value}\n"""							)

    return results






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Union[str, Any]:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
  main()
